Overview

Dataset statistics

Number of variables30
Number of observations56
Missing cells162
Missing cells (%)9.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.9 KiB
Average record size in memory235.4 B

Variable types

DateTime1
Categorical3
Numeric26

Alerts

Mood_num is highly correlated with Mood_bin_num and 1 other fieldsHigh correlation
Mood_bin_num is highly correlated with Mood_num and 1 other fieldsHigh correlation
Friends is highly correlated with Mood_num and 1 other fieldsHigh correlation
Stress is highly correlated with PreviousNightMoodHigh correlation
PreviousNightMood is highly correlated with StressHigh correlation
GeoLatitude is highly correlated with GeoLongitude and 1 other fieldsHigh correlation
GeoLongitude is highly correlated with GeoLatitude and 1 other fieldsHigh correlation
AtHome is highly correlated with GeoLatitude and 1 other fieldsHigh correlation
Mood_num is highly correlated with Mood_bin_num and 1 other fieldsHigh correlation
Mood_bin_num is highly correlated with Mood_num and 1 other fieldsHigh correlation
Friends is highly correlated with Mood_num and 1 other fieldsHigh correlation
Stress is highly correlated with PreviousNightMoodHigh correlation
PreviousNightMood is highly correlated with StressHigh correlation
GeoLatitude is highly correlated with GeoLongitudeHigh correlation
GeoLongitude is highly correlated with GeoLatitudeHigh correlation
Mood_num is highly correlated with Mood_bin_num and 1 other fieldsHigh correlation
Mood_bin_num is highly correlated with Mood_num and 1 other fieldsHigh correlation
Friends is highly correlated with Mood_num and 1 other fieldsHigh correlation
GeoLatitude is highly correlated with GeoLongitude and 1 other fieldsHigh correlation
GeoLongitude is highly correlated with GeoLatitude and 1 other fieldsHigh correlation
AtHome is highly correlated with GeoLatitude and 1 other fieldsHigh correlation
Mood is highly correlated with Mood_binHigh correlation
Mood_bin is highly correlated with MoodHigh correlation
Date is highly correlated with Mood and 28 other fieldsHigh correlation
Mood is highly correlated with Date and 5 other fieldsHigh correlation
Mood_num is highly correlated with Date and 5 other fieldsHigh correlation
Mood_bin is highly correlated with Date and 4 other fieldsHigh correlation
Mood_bin_num is highly correlated with Date and 4 other fieldsHigh correlation
Time is highly correlated with Date and 4 other fieldsHigh correlation
Day is highly correlated with Date and 2 other fieldsHigh correlation
Entertainment is highly correlated with Date and 1 other fieldsHigh correlation
Exercise is highly correlated with Date and 1 other fieldsHigh correlation
Family is highly correlated with Date and 1 other fieldsHigh correlation
Food is highly correlated with DateHigh correlation
Friends is highly correlated with Date and 4 other fieldsHigh correlation
Hobby is highly correlated with Date and 2 other fieldsHigh correlation
Love is highly correlated with DateHigh correlation
Music is highly correlated with Date and 2 other fieldsHigh correlation
NightOut is highly correlated with DateHigh correlation
Projects is highly correlated with DateHigh correlation
School is highly correlated with DateHigh correlation
SelfCare is highly correlated with Date and 1 other fieldsHigh correlation
Sleep is highly correlated with DateHigh correlation
CaffeineCups is highly correlated with DateHigh correlation
AlcoholDrinks is highly correlated with Date and 5 other fieldsHigh correlation
Stress is highly correlated with Date and 4 other fieldsHigh correlation
Sleepiness is highly correlated with Date and 3 other fieldsHigh correlation
PreviousNightMood is highly correlated with Date and 1 other fieldsHigh correlation
GeoLatitude is highly correlated with Date and 2 other fieldsHigh correlation
GeoLongitude is highly correlated with Date and 4 other fieldsHigh correlation
StressR7DM is highly correlated with Date and 3 other fieldsHigh correlation
SleepinessR7DM is highly correlated with Date and 2 other fieldsHigh correlation
AtHome is highly correlated with Date and 2 other fieldsHigh correlation
Mood has 3 (5.4%) missing values Missing
Mood_num has 3 (5.4%) missing values Missing
Mood_bin has 3 (5.4%) missing values Missing
Mood_bin_num has 3 (5.4%) missing values Missing
Time has 3 (5.4%) missing values Missing
Day has 3 (5.4%) missing values Missing
Entertainment has 3 (5.4%) missing values Missing
Exercise has 3 (5.4%) missing values Missing
Family has 3 (5.4%) missing values Missing
Food has 3 (5.4%) missing values Missing
Friends has 3 (5.4%) missing values Missing
Hobby has 3 (5.4%) missing values Missing
Love has 3 (5.4%) missing values Missing
Music has 3 (5.4%) missing values Missing
NightOut has 3 (5.4%) missing values Missing
Projects has 3 (5.4%) missing values Missing
School has 3 (5.4%) missing values Missing
SelfCare has 3 (5.4%) missing values Missing
Sleep has 3 (5.4%) missing values Missing
CaffeineCups has 11 (19.6%) missing values Missing
AlcoholDrinks has 11 (19.6%) missing values Missing
Stress has 11 (19.6%) missing values Missing
Sleepiness has 11 (19.6%) missing values Missing
PreviousNightMood has 19 (33.9%) missing values Missing
GeoLatitude has 11 (19.6%) missing values Missing
GeoLongitude has 11 (19.6%) missing values Missing
StressR7DM has 10 (17.9%) missing values Missing
SleepinessR7DM has 10 (17.9%) missing values Missing
Day is uniformly distributed Uniform
Date has unique values Unique
Mood_num has 4 (7.1%) zeros Zeros
Mood_bin_num has 23 (41.1%) zeros Zeros
Entertainment has 52 (92.9%) zeros Zeros
Exercise has 42 (75.0%) zeros Zeros
Family has 42 (75.0%) zeros Zeros
Food has 47 (83.9%) zeros Zeros
Friends has 30 (53.6%) zeros Zeros
Hobby has 50 (89.3%) zeros Zeros
Love has 44 (78.6%) zeros Zeros
Music has 52 (92.9%) zeros Zeros
NightOut has 50 (89.3%) zeros Zeros
Projects has 36 (64.3%) zeros Zeros
School has 21 (37.5%) zeros Zeros
SelfCare has 49 (87.5%) zeros Zeros
Sleep has 46 (82.1%) zeros Zeros
CaffeineCups has 39 (69.6%) zeros Zeros
AlcoholDrinks has 40 (71.4%) zeros Zeros
Stress has 2 (3.6%) zeros Zeros
GeoLatitude has 5 (8.9%) zeros Zeros
GeoLongitude has 5 (8.9%) zeros Zeros
AtHome has 48 (85.7%) zeros Zeros

Reproduction

Analysis started2022-11-29 11:04:11.023629
Analysis finished2022-11-29 11:05:50.217093
Duration1 minute and 39.19 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Date
Date

HIGH CORRELATION
UNIQUE

Distinct56
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size896.0 B
Minimum2022-10-01 00:00:00
Maximum2022-11-25 00:00:00
2022-11-29T11:05:50.361089image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:05:50.567091image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Mood
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)9.4%
Missing3
Missing (%)5.4%
Memory size716.0 B
Good
22 
Okay
14 
Terrific
Bad
Terrible

Length

Max length8
Median length4
Mean length4.811320755
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOkay
2nd rowBad
3rd rowTerrific
4th rowTerrific
5th rowOkay

Common Values

ValueCountFrequency (%)
Good22
39.3%
Okay14
25.0%
Terrific8
 
14.3%
Bad5
 
8.9%
Terrible4
 
7.1%
(Missing)3
 
5.4%

Length

2022-11-29T11:05:50.721578image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-11-29T11:05:50.824531image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
good22
41.5%
okay14
26.4%
terrific8
 
15.1%
bad5
 
9.4%
terrible4
 
7.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Mood_num
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct5
Distinct (%)9.4%
Missing3
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean2.471698113
Minimum0
Maximum4
Zeros4
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size896.0 B
2022-11-29T11:05:50.968498image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q33
95-th percentile4
Maximum4
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.102510659
Coefficient of variation (CV)0.4460539308
Kurtosis0.01508947288
Mean2.471698113
Median Absolute Deviation (MAD)1
Skewness-0.6860867097
Sum131
Variance1.215529753
MonotonicityNot monotonic
2022-11-29T11:05:51.090792image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%)
322
39.3%
214
25.0%
48
 
14.3%
15
 
8.9%
04
 
7.1%
(Missing)3
 
5.4%
ValueCountFrequency (%)
04
 
7.1%
15
 
8.9%
214
25.0%
322
39.3%
48
 
14.3%
ValueCountFrequency (%)
48
 
14.3%
322
39.3%
214
25.0%
15
 
8.9%
04
 
7.1%

Mood_bin
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)3.8%
Missing3
Missing (%)5.4%
Memory size628.0 B
Good
30 
Bad
23 

Length

Max length4
Median length4
Mean length3.566037736
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBad
2nd rowBad
3rd rowGood
4th rowGood
5th rowBad

Common Values

ValueCountFrequency (%)
Good30
53.6%
Bad23
41.1%
(Missing)3
 
5.4%

Length

2022-11-29T11:05:51.226534image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-11-29T11:05:51.321579image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
good30
56.6%
bad23
43.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Mood_bin_num
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct2
Distinct (%)3.8%
Missing3
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean0.5660377358
Minimum0
Maximum1
Zeros23
Zeros (%)41.1%
Negative0
Negative (%)0.0%
Memory size896.0 B
2022-11-29T11:05:51.391543image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5003627131
Coefficient of variation (CV)0.8839741266
Kurtosis-2.001800512
Mean0.5660377358
Median Absolute Deviation (MAD)0
Skewness-0.2743108116
Sum30
Variance0.2503628447
MonotonicityNot monotonic
2022-11-29T11:05:51.534544image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
130
53.6%
023
41.1%
(Missing)3
 
5.4%
ValueCountFrequency (%)
023
41.1%
130
53.6%
ValueCountFrequency (%)
130
53.6%
023
41.1%

Time
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct47
Distinct (%)88.7%
Missing3
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean18.02861635
Minimum9.333333333
Maximum23.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size896.0 B
2022-11-29T11:05:51.751768image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum9.333333333
5-th percentile14.85333333
Q116.11666667
median17.05
Q320.3
95-th percentile22.88666667
Maximum23.25
Range13.91666667
Interquartile range (IQR)4.183333333

Descriptive statistics

Standard deviation2.938706794
Coefficient of variation (CV)0.1630023479
Kurtosis0.2629512044
Mean18.02861635
Median Absolute Deviation (MAD)1.25
Skewness0.01829521002
Sum955.5166667
Variance8.635997621
MonotonicityNot monotonic
2022-11-29T11:05:51.953253image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
164
 
7.1%
16.116666673
 
5.4%
16.066666672
 
3.6%
17.783333331
 
1.8%
16.083333331
 
1.8%
15.933333331
 
1.8%
16.816666671
 
1.8%
18.833333331
 
1.8%
13.533333331
 
1.8%
16.41
 
1.8%
Other values (37)37
66.1%
(Missing)3
 
5.4%
ValueCountFrequency (%)
9.3333333331
 
1.8%
12.983333331
 
1.8%
13.533333331
 
1.8%
15.733333331
 
1.8%
15.81
 
1.8%
15.933333331
 
1.8%
164
7.1%
16.066666672
3.6%
16.083333331
 
1.8%
16.116666673
5.4%
ValueCountFrequency (%)
23.251
1.8%
23.183333331
1.8%
22.916666671
1.8%
22.866666671
1.8%
22.766666671
1.8%
22.733333331
1.8%
22.716666671
1.8%
22.151
1.8%
21.751
1.8%
211
1.8%

Day
Categorical

HIGH CORRELATION
MISSING
UNIFORM

Distinct7
Distinct (%)13.2%
Missing3
Missing (%)5.4%
Memory size860.0 B
Monday
Tuesday
Saturday
Sunday
Wednesday
Other values (2)
14 

Length

Max length9
Median length7
Mean length7.113207547
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSaturday
2nd rowSunday
3rd rowMonday
4th rowTuesday
5th rowWednesday

Common Values

ValueCountFrequency (%)
Monday8
14.3%
Tuesday8
14.3%
Saturday8
14.3%
Sunday8
14.3%
Wednesday7
12.5%
Thursday7
12.5%
Friday7
12.5%
(Missing)3
 
5.4%

Length

2022-11-29T11:05:52.114340image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-11-29T11:05:52.213256image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
monday8
15.1%
tuesday8
15.1%
saturday8
15.1%
sunday8
15.1%
wednesday7
13.2%
thursday7
13.2%
friday7
13.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Entertainment
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct2
Distinct (%)3.8%
Missing3
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean0.01886792453
Minimum0
Maximum1
Zeros52
Zeros (%)92.9%
Negative0
Negative (%)0.0%
Memory size896.0 B
2022-11-29T11:05:52.321295image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1373605639
Coefficient of variation (CV)7.280109889
Kurtosis53
Mean0.01886792453
Median Absolute Deviation (MAD)0
Skewness7.280109889
Sum1
Variance0.01886792453
MonotonicityNot monotonic
2022-11-29T11:05:52.479258image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
052
92.9%
11
 
1.8%
(Missing)3
 
5.4%
ValueCountFrequency (%)
052
92.9%
11
 
1.8%
ValueCountFrequency (%)
11
 
1.8%
052
92.9%

Exercise
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct2
Distinct (%)3.8%
Missing3
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean0.2075471698
Minimum0
Maximum1
Zeros42
Zeros (%)75.0%
Negative0
Negative (%)0.0%
Memory size896.0 B
2022-11-29T11:05:52.598734image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4094316213
Coefficient of variation (CV)1.972715994
Kurtosis0.2105423988
Mean0.2075471698
Median Absolute Deviation (MAD)0
Skewness1.484602326
Sum11
Variance0.1676342525
MonotonicityNot monotonic
2022-11-29T11:05:52.715168image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
042
75.0%
111
 
19.6%
(Missing)3
 
5.4%
ValueCountFrequency (%)
042
75.0%
111
 
19.6%
ValueCountFrequency (%)
111
 
19.6%
042
75.0%

Family
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct2
Distinct (%)3.8%
Missing3
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean0.2075471698
Minimum0
Maximum1
Zeros42
Zeros (%)75.0%
Negative0
Negative (%)0.0%
Memory size896.0 B
2022-11-29T11:05:52.845121image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4094316213
Coefficient of variation (CV)1.972715994
Kurtosis0.2105423988
Mean0.2075471698
Median Absolute Deviation (MAD)0
Skewness1.484602326
Sum11
Variance0.1676342525
MonotonicityNot monotonic
2022-11-29T11:05:52.976270image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
042
75.0%
111
 
19.6%
(Missing)3
 
5.4%
ValueCountFrequency (%)
042
75.0%
111
 
19.6%
ValueCountFrequency (%)
111
 
19.6%
042
75.0%

Food
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct2
Distinct (%)3.8%
Missing3
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean0.1132075472
Minimum0
Maximum1
Zeros47
Zeros (%)83.9%
Negative0
Negative (%)0.0%
Memory size896.0 B
2022-11-29T11:05:53.089455image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3198784239
Coefficient of variation (CV)2.825592745
Kurtosis4.484105131
Mean0.1132075472
Median Absolute Deviation (MAD)0
Skewness2.513209734
Sum6
Variance0.1023222061
MonotonicityNot monotonic
2022-11-29T11:05:53.192333image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
047
83.9%
16
 
10.7%
(Missing)3
 
5.4%
ValueCountFrequency (%)
047
83.9%
16
 
10.7%
ValueCountFrequency (%)
16
 
10.7%
047
83.9%

Friends
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct2
Distinct (%)3.8%
Missing3
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean0.4339622642
Minimum0
Maximum1
Zeros30
Zeros (%)53.6%
Negative0
Negative (%)0.0%
Memory size896.0 B
2022-11-29T11:05:53.344337image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5003627131
Coefficient of variation (CV)1.15300973
Kurtosis-2.001800512
Mean0.4339622642
Median Absolute Deviation (MAD)0
Skewness0.2743108116
Sum23
Variance0.2503628447
MonotonicityNot monotonic
2022-11-29T11:05:53.487365image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
030
53.6%
123
41.1%
(Missing)3
 
5.4%
ValueCountFrequency (%)
030
53.6%
123
41.1%
ValueCountFrequency (%)
123
41.1%
030
53.6%

Hobby
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct2
Distinct (%)3.8%
Missing3
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean0.05660377358
Minimum0
Maximum1
Zeros50
Zeros (%)89.3%
Negative0
Negative (%)0.0%
Memory size896.0 B
2022-11-29T11:05:53.601349image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.4
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2332953179
Coefficient of variation (CV)4.121550617
Kurtosis14.13665882
Mean0.05660377358
Median Absolute Deviation (MAD)0
Skewness3.950223415
Sum3
Variance0.05442670537
MonotonicityNot monotonic
2022-11-29T11:05:53.707597image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
050
89.3%
13
 
5.4%
(Missing)3
 
5.4%
ValueCountFrequency (%)
050
89.3%
13
 
5.4%
ValueCountFrequency (%)
13
 
5.4%
050
89.3%

Love
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct2
Distinct (%)3.8%
Missing3
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean0.1698113208
Minimum0
Maximum1
Zeros44
Zeros (%)78.6%
Negative0
Negative (%)0.0%
Memory size896.0 B
2022-11-29T11:05:53.827505image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3790600245
Coefficient of variation (CV)2.232242367
Kurtosis1.326417112
Mean0.1698113208
Median Absolute Deviation (MAD)0
Skewness1.810463951
Sum9
Variance0.1436865022
MonotonicityNot monotonic
2022-11-29T11:05:54.766397image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
044
78.6%
19
 
16.1%
(Missing)3
 
5.4%
ValueCountFrequency (%)
044
78.6%
19
 
16.1%
ValueCountFrequency (%)
19
 
16.1%
044
78.6%

Music
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct2
Distinct (%)3.8%
Missing3
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean0.01886792453
Minimum0
Maximum1
Zeros52
Zeros (%)92.9%
Negative0
Negative (%)0.0%
Memory size896.0 B
2022-11-29T11:05:54.871907image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1373605639
Coefficient of variation (CV)7.280109889
Kurtosis53
Mean0.01886792453
Median Absolute Deviation (MAD)0
Skewness7.280109889
Sum1
Variance0.01886792453
MonotonicityNot monotonic
2022-11-29T11:05:54.977434image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
052
92.9%
11
 
1.8%
(Missing)3
 
5.4%
ValueCountFrequency (%)
052
92.9%
11
 
1.8%
ValueCountFrequency (%)
11
 
1.8%
052
92.9%

NightOut
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct2
Distinct (%)3.8%
Missing3
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean0.05660377358
Minimum0
Maximum1
Zeros50
Zeros (%)89.3%
Negative0
Negative (%)0.0%
Memory size896.0 B
2022-11-29T11:05:55.085466image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.4
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2332953179
Coefficient of variation (CV)4.121550617
Kurtosis14.13665882
Mean0.05660377358
Median Absolute Deviation (MAD)0
Skewness3.950223415
Sum3
Variance0.05442670537
MonotonicityNot monotonic
2022-11-29T11:05:55.192580image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
050
89.3%
13
 
5.4%
(Missing)3
 
5.4%
ValueCountFrequency (%)
050
89.3%
13
 
5.4%
ValueCountFrequency (%)
13
 
5.4%
050
89.3%

Projects
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct2
Distinct (%)3.8%
Missing3
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean0.320754717
Minimum0
Maximum1
Zeros36
Zeros (%)64.3%
Negative0
Negative (%)0.0%
Memory size896.0 B
2022-11-29T11:05:55.302077image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4712334431
Coefficient of variation (CV)1.469139558
Kurtosis-1.430449827
Mean0.320754717
Median Absolute Deviation (MAD)0
Skewness0.7905827251
Sum17
Variance0.2220609579
MonotonicityNot monotonic
2022-11-29T11:05:55.414071image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
036
64.3%
117
30.4%
(Missing)3
 
5.4%
ValueCountFrequency (%)
036
64.3%
117
30.4%
ValueCountFrequency (%)
117
30.4%
036
64.3%

School
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct2
Distinct (%)3.8%
Missing3
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean0.6037735849
Minimum0
Maximum1
Zeros21
Zeros (%)37.5%
Negative0
Negative (%)0.0%
Memory size896.0 B
2022-11-29T11:05:55.527436image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.4937931146
Coefficient of variation (CV)0.8178448461
Kurtosis-1.881722689
Mean0.6037735849
Median Absolute Deviation (MAD)0
Skewness-0.4367948198
Sum32
Variance0.2438316401
MonotonicityNot monotonic
2022-11-29T11:05:55.635485image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
132
57.1%
021
37.5%
(Missing)3
 
5.4%
ValueCountFrequency (%)
021
37.5%
132
57.1%
ValueCountFrequency (%)
132
57.1%
021
37.5%

SelfCare
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct2
Distinct (%)3.8%
Missing3
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean0.07547169811
Minimum0
Maximum1
Zeros49
Zeros (%)87.5%
Negative0
Negative (%)0.0%
Memory size896.0 B
2022-11-29T11:05:55.743444image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2666787612
Coefficient of variation (CV)3.533493586
Kurtosis9.29695078
Mean0.07547169811
Median Absolute Deviation (MAD)0
Skewness3.308673466
Sum4
Variance0.07111756168
MonotonicityNot monotonic
2022-11-29T11:05:55.847533image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
049
87.5%
14
 
7.1%
(Missing)3
 
5.4%
ValueCountFrequency (%)
049
87.5%
14
 
7.1%
ValueCountFrequency (%)
14
 
7.1%
049
87.5%

Sleep
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct2
Distinct (%)3.8%
Missing3
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean0.1320754717
Minimum0
Maximum1
Zeros46
Zeros (%)82.1%
Negative0
Negative (%)0.0%
Memory size896.0 B
2022-11-29T11:05:55.956441image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3418128058
Coefficient of variation (CV)2.588011244
Kurtosis3.121519912
Mean0.1320754717
Median Absolute Deviation (MAD)0
Skewness2.237206651
Sum7
Variance0.1168359942
MonotonicityNot monotonic
2022-11-29T11:05:56.060973image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
046
82.1%
17
 
12.5%
(Missing)3
 
5.4%
ValueCountFrequency (%)
046
82.1%
17
 
12.5%
ValueCountFrequency (%)
17
 
12.5%
046
82.1%

CaffeineCups
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct2
Distinct (%)4.4%
Missing11
Missing (%)19.6%
Infinite0
Infinite (%)0.0%
Mean0.1333333333
Minimum0
Maximum1
Zeros39
Zeros (%)69.6%
Negative0
Negative (%)0.0%
Memory size896.0 B
2022-11-29T11:05:56.167428image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3437758255
Coefficient of variation (CV)2.578318691
Kurtosis3.120368004
Mean0.1333333333
Median Absolute Deviation (MAD)0
Skewness2.232390423
Sum6
Variance0.1181818182
MonotonicityNot monotonic
2022-11-29T11:05:56.276415image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
039
69.6%
16
 
10.7%
(Missing)11
 
19.6%
ValueCountFrequency (%)
039
69.6%
16
 
10.7%
ValueCountFrequency (%)
16
 
10.7%
039
69.6%

AlcoholDrinks
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct4
Distinct (%)8.9%
Missing11
Missing (%)19.6%
Infinite0
Infinite (%)0.0%
Mean0.2
Minimum0
Maximum3
Zeros40
Zeros (%)71.4%
Negative0
Negative (%)0.0%
Memory size896.0 B
2022-11-29T11:05:56.402131image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.8
Maximum3
Range3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6252272314
Coefficient of variation (CV)3.126136157
Kurtosis10.99171861
Mean0.2
Median Absolute Deviation (MAD)0
Skewness3.339834951
Sum9
Variance0.3909090909
MonotonicityNot monotonic
2022-11-29T11:05:56.513743image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=4)
ValueCountFrequency (%)
040
71.4%
12
 
3.6%
22
 
3.6%
31
 
1.8%
(Missing)11
 
19.6%
ValueCountFrequency (%)
040
71.4%
12
 
3.6%
22
 
3.6%
31
 
1.8%
ValueCountFrequency (%)
31
 
1.8%
22
 
3.6%
12
 
3.6%
040
71.4%

Stress
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct17
Distinct (%)37.8%
Missing11
Missing (%)19.6%
Infinite0
Infinite (%)0.0%
Mean0.2906666667
Minimum0
Maximum0.84
Zeros2
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size896.0 B
2022-11-29T11:05:56.649201image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.048
Q10.16
median0.24
Q30.4
95-th percentile0.552
Maximum0.84
Range0.84
Interquartile range (IQR)0.24

Descriptive statistics

Standard deviation0.1750636248
Coefficient of variation (CV)0.6022831128
Kurtosis0.9083290513
Mean0.2906666667
Median Absolute Deviation (MAD)0.12
Skewness0.7940941975
Sum13.08
Variance0.03064727273
MonotonicityNot monotonic
2022-11-29T11:05:56.777309image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0.168
14.3%
0.246
10.7%
0.364
 
7.1%
0.323
 
5.4%
0.123
 
5.4%
0.283
 
5.4%
0.523
 
5.4%
0.22
 
3.6%
02
 
3.6%
0.42
 
3.6%
Other values (7)9
16.1%
(Missing)11
19.6%
ValueCountFrequency (%)
02
 
3.6%
0.041
 
1.8%
0.081
 
1.8%
0.123
 
5.4%
0.168
14.3%
0.22
 
3.6%
0.246
10.7%
0.283
 
5.4%
0.323
 
5.4%
0.364
7.1%
ValueCountFrequency (%)
0.841
 
1.8%
0.641
 
1.8%
0.561
 
1.8%
0.523
5.4%
0.482
3.6%
0.442
3.6%
0.42
3.6%
0.364
7.1%
0.323
5.4%
0.283
5.4%

Sleepiness
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct19
Distinct (%)42.2%
Missing11
Missing (%)19.6%
Infinite0
Infinite (%)0.0%
Mean0.4808888889
Minimum0.12
Maximum0.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size896.0 B
2022-11-29T11:05:56.938329image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0.12
5-th percentile0.168
Q10.36
median0.44
Q30.6
95-th percentile0.864
Maximum0.96
Range0.84
Interquartile range (IQR)0.24

Descriptive statistics

Standard deviation0.2086299706
Coefficient of variation (CV)0.4338423604
Kurtosis-0.4650315186
Mean0.4808888889
Median Absolute Deviation (MAD)0.16
Skewness0.355903593
Sum21.64
Variance0.04352646465
MonotonicityNot monotonic
2022-11-29T11:05:57.155319image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0.446
10.7%
0.565
8.9%
0.45
8.9%
0.284
 
7.1%
0.23
 
5.4%
0.363
 
5.4%
0.683
 
5.4%
0.162
 
3.6%
0.882
 
3.6%
0.62
 
3.6%
Other values (9)10
17.9%
(Missing)11
19.6%
ValueCountFrequency (%)
0.121
 
1.8%
0.162
 
3.6%
0.23
5.4%
0.284
7.1%
0.321
 
1.8%
0.363
5.4%
0.45
8.9%
0.446
10.7%
0.481
 
1.8%
0.521
 
1.8%
ValueCountFrequency (%)
0.961
 
1.8%
0.882
 
3.6%
0.81
 
1.8%
0.762
 
3.6%
0.721
 
1.8%
0.683
5.4%
0.641
 
1.8%
0.62
 
3.6%
0.565
8.9%
0.521
 
1.8%

PreviousNightMood
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct15
Distinct (%)40.5%
Missing19
Missing (%)33.9%
Infinite0
Infinite (%)0.0%
Mean15.02702703
Minimum8
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size896.0 B
2022-11-29T11:05:57.320318image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile8.8
Q111
median15
Q318
95-th percentile23.2
Maximum24
Range16
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.46894523
Coefficient of variation (CV)0.2973938373
Kurtosis-0.550457101
Mean15.02702703
Median Absolute Deviation (MAD)3
Skewness0.447034446
Sum556
Variance19.97147147
MonotonicityNot monotonic
2022-11-29T11:05:57.477333image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
117
 
12.5%
155
 
8.9%
133
 
5.4%
183
 
5.4%
163
 
5.4%
82
 
3.6%
242
 
3.6%
172
 
3.6%
222
 
3.6%
92
 
3.6%
Other values (5)6
 
10.7%
(Missing)19
33.9%
ValueCountFrequency (%)
82
 
3.6%
92
 
3.6%
117
12.5%
121
 
1.8%
133
5.4%
142
 
3.6%
155
8.9%
163
5.4%
172
 
3.6%
183
5.4%
ValueCountFrequency (%)
242
 
3.6%
231
 
1.8%
222
 
3.6%
211
 
1.8%
191
 
1.8%
183
5.4%
172
 
3.6%
163
5.4%
155
8.9%
142
 
3.6%

GeoLatitude
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct21
Distinct (%)46.7%
Missing11
Missing (%)19.6%
Infinite0
Infinite (%)0.0%
Mean47.52258714
Minimum0
Maximum53.78028447
Zeros5
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size896.0 B
2022-11-29T11:05:57.663317image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q153.38352773
median53.38357832
Q353.38357832
95-th percentile53.78026661
Maximum53.78028447
Range53.78028447
Interquartile range (IQR)5.059 × 10-5

Descriptive statistics

Standard deviation16.99230175
Coefficient of variation (CV)0.3575626408
Kurtosis4.767905729
Mean47.52258714
Median Absolute Deviation (MAD)5.059 × 10-5
Skewness-2.560652251
Sum2138.516421
Variance288.7383189
MonotonicityNot monotonic
2022-11-29T11:05:57.850319image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
53.3835783218
32.1%
05
 
8.9%
53.383527734
 
7.1%
53.383464041
 
1.8%
53.383489011
 
1.8%
53.780183721
 
1.8%
53.383527121
 
1.8%
53.383527751
 
1.8%
53.383556221
 
1.8%
53.383466371
 
1.8%
Other values (11)11
19.6%
(Missing)11
19.6%
ValueCountFrequency (%)
05
8.9%
53.383452681
 
1.8%
53.383464041
 
1.8%
53.383466371
 
1.8%
53.383489011
 
1.8%
53.383527121
 
1.8%
53.383527734
7.1%
53.383527751
 
1.8%
53.383551111
 
1.8%
53.383556221
 
1.8%
ValueCountFrequency (%)
53.780284471
1.8%
53.780267851
1.8%
53.780267541
1.8%
53.780262881
1.8%
53.780260011
1.8%
53.780183721
1.8%
53.780177031
1.8%
53.780124921
1.8%
53.384459271
1.8%
53.383578771
1.8%

GeoLongitude
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct22
Distinct (%)48.9%
Missing11
Missing (%)19.6%
Infinite0
Infinite (%)0.0%
Mean-5.778020817
Minimum-7.46957665
Maximum0
Zeros5
Zeros (%)8.9%
Negative40
Negative (%)71.4%
Memory size896.0 B
2022-11-29T11:05:58.031320image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-7.46957665
5-th percentile-7.469355483
Q1-6.258099324
median-6.258099324
Q3-6.257778772
95-th percentile0
Maximum0
Range7.46957665
Interquartile range (IQR)0.000320552

Descriptive statistics

Standard deviation2.11694638
Coefficient of variation (CV)-0.3663791542
Kurtosis4.099642159
Mean-5.778020817
Median Absolute Deviation (MAD)0.000320518
Skewness2.312166097
Sum-260.0109368
Variance4.481461975
MonotonicityNot monotonic
2022-11-29T11:05:58.215320image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
-6.25809932418
32.1%
05
 
8.9%
-6.2577788063
 
5.4%
-6.2575241221
 
1.8%
-6.25970221
 
1.8%
-6.2577787721
 
1.8%
-7.4692369631
 
1.8%
-6.2577785151
 
1.8%
-6.2577812711
 
1.8%
-6.2580275061
 
1.8%
Other values (12)12
21.4%
(Missing)11
19.6%
ValueCountFrequency (%)
-7.469576651
1.8%
-7.4694418621
1.8%
-7.4693724981
1.8%
-7.4692874221
1.8%
-7.4692539961
1.8%
-7.4692483971
1.8%
-7.469238871
1.8%
-7.4692369631
1.8%
-6.25970221
1.8%
-6.2580997671
1.8%
ValueCountFrequency (%)
05
8.9%
-6.2575241221
 
1.8%
-6.2575609641
 
1.8%
-6.2575716661
 
1.8%
-6.2576180531
 
1.8%
-6.2577130251
 
1.8%
-6.2577785151
 
1.8%
-6.2577787721
 
1.8%
-6.2577788063
5.4%
-6.2577812711
 
1.8%

StressR7DM
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct38
Distinct (%)82.6%
Missing10
Missing (%)17.9%
Infinite0
Infinite (%)0.0%
Mean0.2954016563
Minimum0.2171428571
Maximum0.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size896.0 B
2022-11-29T11:05:58.435318image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0.2171428571
5-th percentile0.2289285714
Q10.2578571429
median0.2883333333
Q30.3334285714
95-th percentile0.3871428571
Maximum0.43
Range0.2128571429
Interquartile range (IQR)0.07557142857

Descriptive statistics

Standard deviation0.05055461478
Coefficient of variation (CV)0.1711385624
Kurtosis-0.1002169023
Mean0.2954016563
Median Absolute Deviation (MAD)0.035
Skewness0.6729420828
Sum13.58847619
Variance0.002555769076
MonotonicityNot monotonic
2022-11-29T11:05:58.613354image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0.29142857142
 
3.6%
0.26666666672
 
3.6%
0.24666666672
 
3.6%
0.28666666672
 
3.6%
0.26857142862
 
3.6%
0.25333333332
 
3.6%
0.3362
 
3.6%
0.30666666672
 
3.6%
0.30285714291
 
1.8%
0.28571428571
 
1.8%
Other values (28)28
50.0%
(Missing)10
 
17.9%
ValueCountFrequency (%)
0.21714285711
1.8%
0.221
1.8%
0.22857142861
1.8%
0.231
1.8%
0.23428571431
1.8%
0.241
1.8%
0.24666666672
3.6%
0.25142857141
1.8%
0.25333333332
3.6%
0.25714285711
1.8%
ValueCountFrequency (%)
0.431
1.8%
0.391
1.8%
0.38857142861
1.8%
0.38285714291
1.8%
0.3761
1.8%
0.361
1.8%
0.35333333331
1.8%
0.351
1.8%
0.34666666671
1.8%
0.34285714291
1.8%

SleepinessR7DM
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct45
Distinct (%)97.8%
Missing10
Missing (%)17.9%
Infinite0
Infinite (%)0.0%
Mean0.4857453416
Minimum0.3485714286
Maximum0.6866666667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size896.0 B
2022-11-29T11:05:58.870320image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0.3485714286
5-th percentile0.3616666667
Q10.3964285714
median0.4564285714
Q30.5745238095
95-th percentile0.6571428571
Maximum0.6866666667
Range0.3380952381
Interquartile range (IQR)0.1780952381

Descriptive statistics

Standard deviation0.1023493325
Coefficient of variation (CV)0.2107057417
Kurtosis-1.003790074
Mean0.4857453416
Median Absolute Deviation (MAD)0.07442857143
Skewness0.5173229513
Sum22.34428571
Variance0.01047538586
MonotonicityNot monotonic
2022-11-29T11:05:59.097322image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0.38666666672
 
3.6%
0.63333333331
 
1.8%
0.55333333331
 
1.8%
0.61
 
1.8%
0.56666666671
 
1.8%
0.61142857141
 
1.8%
0.65714285711
 
1.8%
0.65714285711
 
1.8%
0.68571428571
 
1.8%
0.68666666671
 
1.8%
Other values (35)35
62.5%
(Missing)10
 
17.9%
ValueCountFrequency (%)
0.34857142861
1.8%
0.35428571431
1.8%
0.361
1.8%
0.36666666671
1.8%
0.371
1.8%
0.37142857141
1.8%
0.37333333331
1.8%
0.381
1.8%
0.3841
1.8%
0.38666666672
3.6%
ValueCountFrequency (%)
0.68666666671
1.8%
0.68571428571
1.8%
0.65714285711
1.8%
0.65714285711
1.8%
0.65333333331
1.8%
0.63333333331
1.8%
0.6161
1.8%
0.61142857141
1.8%
0.61
1.8%
0.591
1.8%

AtHome
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1428571429
Minimum0
Maximum1
Zeros48
Zeros (%)85.7%
Negative0
Negative (%)0.0%
Memory size672.0 B
2022-11-29T11:05:59.297318image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3530939318
Coefficient of variation (CV)2.471657523
Kurtosis2.488644305
Mean0.1428571429
Median Absolute Deviation (MAD)0
Skewness2.097857465
Sum8
Variance0.1246753247
MonotonicityNot monotonic
2022-11-29T11:05:59.459319image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
048
85.7%
18
 
14.3%
ValueCountFrequency (%)
048
85.7%
18
 
14.3%
ValueCountFrequency (%)
18
 
14.3%
048
85.7%

Interactions

2022-11-29T11:05:44.268614image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:04:15.391501image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:04:19.184511image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:04:23.063538image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:04:26.676402image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:04:30.043470image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:04:35.142019image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:04:38.403843image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:04:41.686367image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:04:45.390553image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:04:49.020691image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:04:52.840565image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:04:56.263593image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:04:59.607223image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:05:02.997978image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:05:06.393923image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:05:10.341262image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:05:13.590646image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:05:16.854025image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:05:20.359144image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:05:23.556171image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:05:26.684868image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:05:30.825249image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:05:34.036703image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:05:37.452092image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2022-11-29T11:05:44.392614image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:04:15.562804image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:04:19.349504image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:04:23.233505image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:04:26.835441image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:04:30.190453image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:04:35.298072image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:04:38.541811image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:04:41.829417image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:04:45.524557image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:04:49.589687image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:04:53.016529image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:04:56.413465image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:04:59.759758image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:05:03.144400image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:05:07.090923image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:05:10.492349image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:05:13.729599image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:05:16.999004image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:05:20.498093image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:05:23.699185image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:05:26.832255image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:05:30.978285image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:05:34.168669image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:05:37.600722image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:05:40.792666image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:05:44.501643image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:04:15.707614image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:04:19.522505image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:04:23.364528image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2022-11-29T11:04:59.893798image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2022-11-29T11:05:07.214016image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:05:10.624379image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2022-11-29T11:05:00.156762image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:05:03.540370image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:05:07.487467image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:05:10.872349image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:05:14.102824image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:05:17.414996image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:05:20.854451image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:05:24.057260image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:05:27.220282image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2022-11-29T11:04:30.703444image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:04:35.798293image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:04:39.032658image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2022-11-29T11:04:42.366354image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2022-11-29T11:05:44.127616image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Correlations

2022-11-29T11:05:59.654350image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-29T11:06:00.126324image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-29T11:06:00.573317image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-29T11:06:00.971348image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-29T11:06:01.198320image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-29T11:05:47.657282image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-29T11:05:48.463206image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-11-29T11:05:49.075169image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-11-29T11:05:49.989167image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

DateMoodMood_numMood_binMood_bin_numTimeDayEntertainmentExerciseFamilyFoodFriendsHobbyLoveMusicNightOutProjectsSchoolSelfCareSleepCaffeineCupsAlcoholDrinksStressSleepinessPreviousNightMoodGeoLatitudeGeoLongitudeStressR7DMSleepinessR7DMAtHome
02022-10-01Okay2.0Bad0.016.200000Saturday0.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.160.16NaN53.780268-7.469254NaNNaN1
12022-10-02Bad1.0Bad0.020.750000Sunday0.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.240.44NaN53.780260-7.469239NaNNaN1
22022-10-03Terrific4.0Good1.018.350000Monday0.01.01.01.01.01.01.00.00.01.01.01.01.00.00.00.520.56NaN53.780263-7.469287NaNNaN1
32022-10-04Terrific4.0Good1.022.733333Tuesday0.00.00.00.00.00.01.00.00.00.00.01.00.00.00.00.240.32NaN53.383453-6.2575610.2900000.3700000
42022-10-05Okay2.0Bad0.022.150000Wednesday0.00.00.00.00.00.00.00.00.00.01.00.00.00.01.00.160.44NaN53.383464-6.2575240.2640000.3840000
52022-10-06Terrible0.0Bad0.016.350000Thursday0.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.520.40NaN53.383489-6.2577130.3066670.3866670
62022-10-07Good3.0Good1.017.316667Friday0.00.01.00.01.00.00.00.00.00.01.00.00.0NaNNaNNaNNaNNaNNaNNaN0.3066670.3866670
72022-10-08Good3.0Good1.018.866667Saturday0.00.01.00.00.00.00.00.00.00.00.01.00.0NaNNaNNaNNaNNaNNaNNaN0.3360000.4320000
82022-10-09Bad1.0Bad0.022.916667Sunday1.00.00.00.00.00.01.00.00.00.00.00.00.01.00.00.240.40NaN53.780268-7.4692480.3360000.4240001
92022-10-10Bad1.0Bad0.012.983333Monday0.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.640.64NaN53.383551-6.2576180.3600000.4400000

Last rows

DateMoodMood_numMood_binMood_bin_numTimeDayEntertainmentExerciseFamilyFoodFriendsHobbyLoveMusicNightOutProjectsSchoolSelfCareSleepCaffeineCupsAlcoholDrinksStressSleepinessPreviousNightMoodGeoLatitudeGeoLongitudeStressR7DMSleepinessR7DMAtHome
462022-11-16Okay2.0Bad0.017.050000Wednesday0.00.00.00.00.00.00.00.00.01.01.00.00.01.00.00.360.1218.053.383466-6.2575720.2600000.5066670
472022-11-17Okay2.0Bad0.016.233333Thursday0.00.00.00.00.00.00.00.00.01.01.00.00.00.00.00.200.4416.053.383556-6.2580280.2866670.4333330
482022-11-18Terrific4.0Good1.020.816667Friday0.00.00.00.01.00.00.00.01.00.00.00.00.00.00.00.120.2022.053.383528-6.2577810.2800000.3933330
492022-11-19Good3.0Good1.020.266667Saturday0.00.00.01.00.00.00.01.00.00.01.00.00.00.02.00.360.7611.053.383527-6.2577790.2466670.3600000
502022-11-20Okay2.0Bad0.016.183333Sunday0.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.520.2811.053.780184-7.4692370.2857140.3485711
512022-11-21Bad1.0Bad0.020.633333Monday0.01.00.00.00.00.00.00.00.00.00.00.01.00.00.00.400.4011.053.383528-6.2577790.3028570.3542860
522022-11-22Okay2.0Bad0.019.016667Tuesday0.00.00.00.00.00.00.00.00.01.01.00.00.00.00.00.320.8011.00.0000000.0000000.3257140.4285710
532022-11-23NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.00.480.209.053.383528-6.2577790.3428570.4400000
542022-11-24NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.00.480.6815.053.383528-6.2577790.3828570.4742860
552022-11-25NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.00.00.160.2815.053.383528-6.2577790.3885710.4857140